{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from utils.tracklet_tools import read_objects\n",
    "import os\n",
    "# os.environ['DISPLAY'] = ':0'\n",
    "from collections import defaultdict, OrderedDict\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "print(os.path.exists('tracklet_files/cmax01.xml'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_x_y(tracklet_file):\n",
    "    objects = read_objects(tracklet_file)\n",
    "    tzs = defaultdict(list)\n",
    "    for one_frame_objs in objects:\n",
    "        for obj in one_frame_objs:\n",
    "            tzs[obj[0]].append(obj[1][2])\n",
    "\n",
    "    frame_ids = []\n",
    "    tz_v = []\n",
    "\n",
    "    # tzs = OrderedDict(sorted(tzs.items()))\n",
    "\n",
    "    for k, v in tzs.items():\n",
    "        frame_ids.append(k)\n",
    "        tz_v.append(v[0])\n",
    "    \n",
    "    return frame_ids, tz_v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_x_y_2(tracklet_file):\n",
    "    objects = read_objects(tracklet_file)\n",
    "    tzs = defaultdict(list)\n",
    "    \n",
    "    for obs in objects:\n",
    "        # get the car's content:\n",
    "        # for frame\n",
    "        for i, pose in enumerate(obs):\n",
    "            tzs[i].append(pose[1][2])\n",
    "\n",
    "    frame_ids = []\n",
    "    tz_v = []\n",
    "\n",
    "    # tzs = OrderedDict(sorted(tzs.items()))\n",
    "\n",
    "    for k, v in tzs.items():\n",
    "        frame_ids.append(k)\n",
    "        tz_v.append(v)\n",
    "    \n",
    "    return frame_ids, tz_v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pred_x, pred_y = get_x_y('tracklet_files/cmax01.xml')\n",
    "true_x, true_y = get_x_y_2('tracklet_files/cmax01_corrected.xml')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "160 160\n",
      "1168 1168\n"
     ]
    }
   ],
   "source": [
    "print(len(pred_x), len(pred_y))\n",
    "print(len(true_x), len(true_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fc347218a20>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YZmaWxYFhZmZZHBhmZpbFgWFmZlkcGGZmlsWBYWZmWRwYZmaWxYFhZmZZHBhmZpbFgWFm\nZlkcGGZmlqVUYEiaK2m7pH3pfU6DOu+TtLvm9Yqk5WnZsKRnJO2V9A1Js1L51ZJerPnMF8u008zM\nyivbw1gH7IyIxcDOND9KRDwaEUsiYglwDXACeCQtHgbeBrwLOAe4oeajj1U/FxFfKtlOMzMrqWxg\nLAM2p+nNwPIW9VcA2yLiBEBEPBQJ8CPg4pLtMTOzCVI2MC6IiMMA6f38FvVXAvfWF6ahqE8Cf1tT\nfJWkPZK2SXpHyXaamVlJfa0qSNoBXNhg0fp2vkjSQoqhp4cbLN4A/ENEPJbmfwIsiojjkq4DHgQW\nN1nvEDAE0N/f306TzMysDS0DIyKubbZM0nOSFkbE4RQIR8ZY1ceBByLi1bp1/DmwAPhszXe+VDP9\nkKQNkuZHxLEG7dsEbAKoVCrRanvMzGx8yg5JbQVWp+nVwJYx6q6ibjhK0g3AB4FVEfF6TfmFkpSm\nr0jtfL5kW83MrISygXELsFTSPmBpmkdSRdKd1UqSBoBLgO/Xff524ALg8brLZ1cAeyXtAW4DVqYT\n42Zm1iWaTsfhSqUSIyMj3W6GmVlPkbQrIiqt6vmX3mZmlsWBYWZmWRwYZmaWxYFhZmZZHBhmZpbF\ngWFmZlkcGGZmlsWBYWZmWRwYZmaWxYFhZmZZHBhmZpbFgWFmZlkcGGZmlsWBYWZmWRwYZmaWxYFh\nZmZZHBhmZpbFgWFmZlkcGGZmlsWBYWZmWUoFhqS5krZL2pfe5zSo8z5Ju2ter0hanpZ9S9LPa5Yt\nSeWSdJuk/ZJ+KunyMu00M7PyyvYw1gE7I2IxsDPNjxIRj0bEkohYAlwDnAAeqanyZ9XlEbE7lX0Y\nWJxeQ8DGku00M7OSygbGMmBzmt4MLG9RfwWwLSJOZKz3rij8ADhP0sJyTTUzszLKBsYFEXEYIL2f\n36L+SuDeurKb07DTVySdncouAn5RU+dQKjuNpCFJI5JGjh492v4WmJlZlpaBIWmHpL0NXsva+aLU\nQ3gX8HBN8U3A24D3AHOBL1SrN1hFNFpvRGyKiEpEVBYsWNBOk8zMrA19rSpExLXNlkl6TtLCiDic\nAuHIGKv6OPBARLxas+7DafJfJX0T+HyaPwRcUvPZi4FftWqrmZlNnLJDUluB1Wl6NbBljLqrqBuO\nqp6XkCSK8x97a9Z7fbpa6krgxZpwMTOzLmjZw2jhFuA7kj4DHAQ+BiCpAnwuIm5I8wMUPYbv131+\nWNICiiGo3cDnUvlDwHXAfoqrqj5dsp1mZlaSIhqeGuhJlUolRkZGut0MM7OeImlXRFRa1fMvvc3M\nLIsDw8zMsjgwzMwsiwPDzMyyODDMzCyLA8PMzLI4MMzMLIsDw8zMsjgwzMwsiwPDzMyyODDMzCyL\nA8PMzLI4MMzMLIsDw8zMsjgwzMwsiwPDzMyyODDMzCyLA8PMzLI4MMzMLEupwJA0V9J2SfvS+5wG\ndd4naXfN6xVJy9Oyx2rKfyXpwVR+taQXa5Z9sUw7zcysvL6Sn18H7IyIWyStS/NfqK0QEY8CS6AI\nGGA/8Eha9t5qPUnfBbbUfPSxiPhIyfaZmVmHlB2SWgZsTtObgeUt6q8AtkXEidpCSW8CrgEeLNke\nMzObIGUD44KIOAyQ3s9vUX8lcG+D8o9S9FReqim7StIeSdskvaNkO83MrKSWQ1KSdgAXNli0vp0v\nkrQQeBfwcIPFq4A7a+Z/AiyKiOOSrqPoeSxust4hYAigv7+/nSaZmVkbWgZGRFzbbJmk5yQtjIjD\nKRCOjLGqjwMPRMSrdeuYB1xB0cuofudLNdMPSdogaX5EHGvQvk3AJoBKpRKttsfMzMan7JDUVmB1\nml7N6JPW9VbReDjqY8D3IuKVaoGkCyUpTV+R2vl8ybaamVkJZQPjFmCppH3A0jSPpIqk3w4xSRoA\nLgG+32Adjc5rrAD2StoD3AasjAj3HszMukjT6ThcqVRiZGSk280wM+spknZFRKVVPf/S28zMsjgw\nzMwsiwPDzMyyODDMzCyLA8PMzLI4MMzMLIsDw8zMsjgwzMwsiwPDzMyyODDMzCyLA8PMzLI4MMzM\nLIsDw8zMsjgwzMwsiwPDzMyyODDMzCyLA8PMzLI4MGqsXQtS+6+1a7vdcpvOxvv30q8z5zVZx6C+\nyfmaqW/tWti4cXyf3bhx/J81MyurevzZsGFiv8c9jGTTpm63wMxs/CbjGFY6MCTNlbRd0r70PqdJ\nvb+Q9I+SnpZ0mySl8n8r6UlJ++vKs9bbKSdPTuTazcwm1mQcwzrRw1gH7IyIxcDOND+KpD8A/hD4\nN8A7gfcAf5wWbwSGgMXp9aHc9XbSzJkTuXYzs4k1GcewTgTGMmBzmt4MLG9QJ4A3AGcBZwOzgOck\nLQR+JyIej4gA7qr5fM56O2ZoaCLXbmY2sSbjGNaJwLggIg4DpPfz6ytExOPAo8Dh9Ho4Ip4GLgIO\n1VQ9lMqy1ttJGzbAmjUT+Q1mZhNjzZqJP+ENmVdJSdoBXNhg0frMz/8e8Hbg4lS0XdIfAb9pUD1y\n1lmz7iGKIS36+/vb+ehpNmzI+0Mvc0WVWVmTdXAwq5cVGBFxbbNlkp6TtDAiDqchpiMNqn0U+EFE\nHE+f2QZcCdzNqRAhTf8qTeesl4jYBGwCqFQqbYXNeOUGi5nZdNKJIamtwOo0vRrY0qDOQeCPJfVJ\nmkVxwvvpNNT0/yRdma6Our7m8znrNTOzSdKJwLgFWCppH7A0zSOpIunOVOc+4P8ATwJ7gD0R8b/S\nsjXAncD+VGfbWOs1M7PuUHFx0vRQqVRiZGSk280wM+spknZFRKVVPf/S28zMsjgwzMwsiwPDzMyy\nODDMzCzLtDrpLekocKADq5oPHOvAeqaC6bQtML22x9syNZ2J27IoIha0qjStAqNTJI3kXDHQC6bT\ntsD02h5vy9TkbWnOQ1JmZpbFgWFmZlkcGI1Np+fvTadtgem1Pd6Wqcnb0oTPYZiZWRb3MMzMLIsD\no46kD0l6Jj1jfEIfC9sJki6R9Gh6Vvo/SroxlTd8JroKt6Xt+6mky7u7BaeTNFPSE5K+l+bfIumH\naVu+LemsVH52mt+flg90s931JJ0n6T5J/5T2z1W9ul8k/af092uvpHslvaGX9oukb0g6ImlvTVnb\n+0LS6lR/n6TVjb6rS9tya/p79lNJD0g6r2bZTWlbnpH0wZry9o91EeFXegEzKe6YeynF42T3AJd1\nu10t2rwQuDxNvwn4Z+Ay4C+Adal8HfDlNH0dxR2BRfFMkh92exsabNOfAn8FfC/NfwdYmaZvB9ak\n6bXA7Wl6JfDtbre9bjs2Azek6bOA83pxv1A8BfPnwDk1++NTvbRfgD8CLgf21pS1tS+AucDP0vuc\nND1nimzLB4C+NP3lmm25LB3Hzgbeko5vM8d7rOv6X8ap9AKuonh8bHX+JuCmbrerzW3YQnE7+GeA\nhalsIfBMmr4DWFVT/7f1psKL4iFaO4FrgO+lf7THav4x/HYfAQ8DV6XpvlRP3d6G1J7fSQdZ1ZX3\n3H5JgfGLdKDsS/vlg722X4CBuoNsW/sCWAXcUVM+ql43t6Vu2UeB4TQ96hhW3TfjPdZ5SGq06j+M\nqtpnjE95qev/buCHNH8m+lTfxr8E/jPwepqfB7wQEa+l+dr2/nZb0vIXU/2p4FLgKPDNNLx2p6Rz\n6cH9EhG/BP4bxYPQDlP8Oe+iN/dLrXb3xZTdR3X+PaeeK9TRbXFgjKYGZT1xGZmkNwLfBf5jRLw0\nVtUGZVNiGyV9BDgSEbtqixtUjYxl3dZHMWywMSLeDbxMMezRzJTdljS2v4xiSOPNwLnAhxtU7YX9\nkqNZ+6f8dklaD7wGDFeLGlQb97Y4MEY7BFxSM1/7jPEpS8Vjb79L0Q29PxU/p+JZ6Gj0M9Gn8jb+\nIfAnkp4F/ppiWOovgfMkVZ8/X9ve325LWv67wK8ns8FjOAQciogfpvn7KAKkF/fLtcDPI+JoRLwK\n3A/8Ab25X2q1uy+m8j4inYT/CDAYaZyJDm+LA2O0HwOL09UfZ1GcsNva5TaNSZKAr1M8I/2/1yxq\n9kz0rcD16UqQK4EXq93ybouImyLi4ogYoPiz/7uIGAQeBVakavXbUt3GFan+lPgfX0T8X+AXkn4/\nFb0feIoe3C8UQ1FXSpqd/r5Vt6Xn9kuddvfFw8AHJM1Jva4PpLKuk/Qh4AvAn0TEiZpFW4GV6cq1\ntwCLgR8x3mNdt09ETbUXxRUS/0xxBcH6brcno73/jqIr+VNgd3pdRzFmvBPYl97npvoC/gennrFe\n6fY2NNmuqzl1ldSl6S/5fuB/Amen8jek+f1p+aXdbnfdNiwBRtK+eZDiypqe3C/AfwH+CdgL3E1x\n1U3P7BfgXorzL69S/O/6M+PZFxTnB/an16en0LbspzgnUT0G3F5Tf33almeAD9eUt32s8y+9zcws\ni4ekzMwsiwPDzMyyODDMzCyLA8PMzLI4MMzMLIsDw8zMsjgwzMwsiwPDzMyy/H+04fzgAVtTcgAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc3471f8860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.scatter(pred_x, pred_y, c='red')\n",
    "plt.scatter(true_x, true_y, c='blue')\n",
    "# print(tzs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fc346d84e80>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hvm51108C+KL79TCAO2EK/Z8B+IzNvjkpPPVqNNbOhd675DkverutOjwcfY6Aaqs1oJMY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\nqf8Hnuj8i40XxKsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc346e8c710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_x, pred_y = get_x_y('tracklet_files/nissan07.xml')\n",
    "true_x, true_y = get_x_y_2('tracklet_files/nissan07_corrected.xml')\n",
    "plt.figure()\n",
    "plt.scatter(pred_x, pred_y, c='red')\n",
    "plt.scatter(true_x, true_y, c='blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fc346dbb9e8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4f0JImykaBfRpAJ827P82gFtinx8B8EiRc02CixfN+xn/TwhpA5wJnEAQAK+8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\nfwbPRP7CDc28bAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc347207240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_x, pred_y = get_x_y('tracklet_files/suburu05.xml')\n",
    "true_x, true_y = get_x_y_2('tracklet_files/suburu05_corrected.xml')\n",
    "\n",
    "plt.figure()\n",
    "plt.scatter(pred_x, pred_y, c='red')\n",
    "plt.scatter(true_x, true_y, c='blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fc346d54da0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SLpP0pKT9kkp9fWlJT0l6XNJuSZPpvmWSHpC0L709rQRx3i7peUl7Wva1jVOJ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\nAe/8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc346dc5908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_x, pred_y = get_x_y('tracklet_files/suburu04.xml')\n",
    "true_x, true_y = get_x_y_2('tracklet_files/suburu04_corrected.xml')\n",
    "\n",
    "plt.figure()\n",
    "plt.scatter(pred_x, pred_y, c='red')\n",
    "plt.scatter(true_x, true_y, c='blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fc343aa4f28>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EzIJtZqZ/PZEuSDvA3a/uR0Jarrcf2A/BQLB+XrtoavwVKbdaDR54IHkReTj3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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc343be7d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred_x, pred_y = get_x_y('tracklet_files/suburu06.xml')\n",
    "true_x, true_y = get_x_y_2('tracklet_files/suburu06_corrected.xml')\n",
    "\n",
    "plt.figure()\n",
    "plt.scatter(pred_x, pred_y, c='red')\n",
    "plt.scatter(true_x, true_y, c='blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
